Sigalova, O.M., Forneris, M., Stojanovska, F., Zhao, B., Viales, R.R., Rabinowitz, A., Hammal, F., Ballester, B., Zaugg, J.B., Furlong, E.E.M. (2025). Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis.  Genome Res. 35(5): 1138--1153.

FBrf0262267

Research paper

Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F1 crosses with extensive genetic diversity to profile allele-specific binding of four TFs at several time points during Drosophila embryogenesis. Using a combined haplotype test, we identified 9%-18% of TF-bound regions impacted by genetic variation even for essential regulators. By expanding WASP (a tool for allele-specific read mapping) to examine indels, we increased detection of allelically imbalanced peaks by 30%-50%. This fine-grained "mutagenesis" can reconstruct functionalized binding motifs for all factors. To prioritize causal variants, we trained a convolutional neural network (Basenji) to accurately predict binding from DNA sequence. The model can also predict measured allelic imbalance for strong effect variants, providing a mechanistic interpretation for how the variant impacts binding. This reveals unexpected relationships between TFs, including potential cooperative pairs, and mechanisms of tissue-specific recruitment of the ubiquitous factor CTCF.

PMC12047541 (PMC) (EuropePMC)